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Inferring decision rules from evidence, choice, and reaction times

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If you have a question about this talk, please contact Daniel McNamee.

I will present four related projects about decision-making based on one or two evidence streams, focusing on the quantitative relationship between the choice, reaction time, and the contents of evidence.

(1) I will show that we can predict choices based on the reported time of covert decisions (decisions without an immediate motor response) in a perceptual decision-making task. We conclude that subjects terminate the decision when accumulated evidence reaches a threshold even when more evidence is available, and that the reported time of the mental event (threshold crossing) is reliable enough to enable prediction.

(2) For the decisions reported in (1) and for other types of decisions, it has been suggested that the decision thresholds decrease over time. Here I will present an efficient technique to estimate time-varying decision thresholds without an assumption on their shape.

(3) As a step toward more complex decisions, I will discuss perceptual decision-making based on two evidence streams, motion and color. I will show that even when the two evidence streams are available simultaneously at the same location and when only one motor response is required to report the decision, the time to make the combined decision is better explained by the sum of the duration of the two decision processes than by their maximum, suggesting interference. I will also discuss an ongoing project on estimating the degree of interference between the two evidence accumulation processes.

(4) Although in (3) we show that there exists interference between the two decision processes, it is unclear if the interference is at the evidence acquisition stage (sensory), or at the evidence accumulation stage (central). I will present a Bayesian reverse-correlation technique to estimate the time-varying probability of simultaneous evidence acquisition from two evidence streams.

This talk is part of the Computational Neuroscience series.

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